基于机器学习的分类,使用动力学参数和物理特征对泰拳Pomsae侧性能进行分类
Ui-Jae Hwang1, Sung-Hoon Jung2, Ho-Chul Ji3
1Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China.
Sports biomechanics
|July 8, 2025
概括
机器学习模型使用动力学数据和物理功能测试准确地分类泰拳边 (SK) 性能. 侧角度和动态平衡是关键预测因素,改善了客观评估.
科学领域:
- 运动科学 运动科学 运动科学
- 生物力学 生物力学
- 运动中的机器学习
背景情况:
- 传统上,羽毛球的表现评估依赖于主观的评价.
- 需要客观和可量化的指标来提高绩效分类的准确性和一致性.
研究的目的:
- 开发和验证机器学习 (ML) 模型来分类泰拳道Pomsae侧 (SK) 性能.
- 确定可预测SK性能质量的关键动力学和物理功能参数.
主要方法:
- 开发了两种ML模型:一种动力学 (SK和骨盆倾斜角度) 和一种物理功能 (运动范围,Y平衡测试).
- 使用随机森林分类器,并使用曲线下的面积 (AUC) 分析评估性能.
- 40名大学泰拳道运动员进行了SK,他们的表现由专家评估员评估.
主要成果:
- 这两种模型都取得了出色的性能 (AUC = 0.930,精度 = 89.3%).
- 在最大高度的SK角度是动力模型中最强的预测因素.
- 在物理功能模型中,Y平衡测试复合分数是影响最大的参数.
结论:
- ML模型提供了准确和客观的分类,超过传统的主观方法.
- 动力参数 (SK角度) 和物理功能 (动态平衡) 对于性能评估至关重要.
- 这些发现为评估泰拳道Pomsae侧执行提供了定量标准.
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